Abstract In general, data contain noises which come from faulty instruments, flawed measurements or communication. Learning with in the context of classification regression is inevitably affected by data. order to remove greatly reduce impact noises, we introduce ideas fuzzy membership functions and Laplacian twin support vector machine (Lap-TSVM). A formulation linear intuitionistic (IFLap-TSV...

|To design a fuzzy rule-based classi cation system (fuzzy classi er) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high (or e...

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy – Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is the base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into several di...

This paper comments on the recently published work dealing with support vector machine for classification based on fuzzy data [Ji, A.-B., Pang, J.-H., & Qiu, H.-J. (2010). Support vector machine for classification based on fuzzy training data. Expert Systems with Applications 37(4), 3495–3498]. The authors have claimed that their proposed program is a classical convex quadratic program. But, we...

An enhanced classification system for classification of brain tumor from MR images using association of kernels with support vector machine is developed and presented in this paper. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified fuzzy c-means algorithm termed as Penalized fuzzy c-means algorithm is used for image segmentation. The texture an...